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Dynamic probabilistic threshold networks to infer signaling pathways from time-course perturbation data
BACKGROUND: Network inference deals with the reconstruction of molecular networks from experimental data. Given N molecular species, the challenge is to find the underlying network. Due to data limitations, this typically is an ill-posed problem, and requires the integration of prior biological know...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4133630/ https://www.ncbi.nlm.nih.gov/pubmed/25047753 http://dx.doi.org/10.1186/1471-2105-15-250 |
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author | Kiani, Narsis A Kaderali, Lars |
author_facet | Kiani, Narsis A Kaderali, Lars |
author_sort | Kiani, Narsis A |
collection | PubMed |
description | BACKGROUND: Network inference deals with the reconstruction of molecular networks from experimental data. Given N molecular species, the challenge is to find the underlying network. Due to data limitations, this typically is an ill-posed problem, and requires the integration of prior biological knowledge or strong regularization. We here focus on the situation when time-resolved measurements of a system’s response after systematic perturbations are available. RESULTS: We present a novel method to infer signaling networks from time-course perturbation data. We utilize dynamic Bayesian networks with probabilistic Boolean threshold functions to describe protein activation. The model posterior distribution is analyzed using evolutionary MCMC sampling and subsequent clustering, resulting in probability distributions over alternative networks. We evaluate our method on simulated data, and study its performance with respect to data set size and levels of noise. We then use our method to study EGF-mediated signaling in the ERBB pathway. CONCLUSIONS: Dynamic Probabilistic Threshold Networks is a new method to infer signaling networks from time-series perturbation data. It exploits the dynamic response of a system after external perturbation for network reconstruction. On simulated data, we show that the approach outperforms current state of the art methods. On the ERBB data, our approach recovers a significant fraction of the known interactions, and predicts novel mechanisms in the ERBB pathway. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1471-2105-15-250) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-4133630 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-41336302014-08-16 Dynamic probabilistic threshold networks to infer signaling pathways from time-course perturbation data Kiani, Narsis A Kaderali, Lars BMC Bioinformatics Methodology Article BACKGROUND: Network inference deals with the reconstruction of molecular networks from experimental data. Given N molecular species, the challenge is to find the underlying network. Due to data limitations, this typically is an ill-posed problem, and requires the integration of prior biological knowledge or strong regularization. We here focus on the situation when time-resolved measurements of a system’s response after systematic perturbations are available. RESULTS: We present a novel method to infer signaling networks from time-course perturbation data. We utilize dynamic Bayesian networks with probabilistic Boolean threshold functions to describe protein activation. The model posterior distribution is analyzed using evolutionary MCMC sampling and subsequent clustering, resulting in probability distributions over alternative networks. We evaluate our method on simulated data, and study its performance with respect to data set size and levels of noise. We then use our method to study EGF-mediated signaling in the ERBB pathway. CONCLUSIONS: Dynamic Probabilistic Threshold Networks is a new method to infer signaling networks from time-series perturbation data. It exploits the dynamic response of a system after external perturbation for network reconstruction. On simulated data, we show that the approach outperforms current state of the art methods. On the ERBB data, our approach recovers a significant fraction of the known interactions, and predicts novel mechanisms in the ERBB pathway. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1471-2105-15-250) contains supplementary material, which is available to authorized users. BioMed Central 2014-07-22 /pmc/articles/PMC4133630/ /pubmed/25047753 http://dx.doi.org/10.1186/1471-2105-15-250 Text en © Kiani and Kaderali; licensee BioMed Central Ltd. 2014 This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. |
spellingShingle | Methodology Article Kiani, Narsis A Kaderali, Lars Dynamic probabilistic threshold networks to infer signaling pathways from time-course perturbation data |
title | Dynamic probabilistic threshold networks to infer signaling pathways from time-course perturbation data |
title_full | Dynamic probabilistic threshold networks to infer signaling pathways from time-course perturbation data |
title_fullStr | Dynamic probabilistic threshold networks to infer signaling pathways from time-course perturbation data |
title_full_unstemmed | Dynamic probabilistic threshold networks to infer signaling pathways from time-course perturbation data |
title_short | Dynamic probabilistic threshold networks to infer signaling pathways from time-course perturbation data |
title_sort | dynamic probabilistic threshold networks to infer signaling pathways from time-course perturbation data |
topic | Methodology Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4133630/ https://www.ncbi.nlm.nih.gov/pubmed/25047753 http://dx.doi.org/10.1186/1471-2105-15-250 |
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